ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

一般6R機器人的高精度逆運動學優(yōu)化算法
CSTR:
作者:
作者單位:

作者簡介:

通訊作者:

中圖分類號:

基金項目:


Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 圖/表
  • |
  • 訪問統(tǒng)計
  • |
  • 參考文獻
  • |
  • 相似文獻
  • |
  • 引證文獻
  • |
  • 資源附件
  • |
  • 文章評論
    摘要:

    為提高一般6R機器人逆運動學算法的精度和效率,,提出一種基于符號運算和矩陣分解的優(yōu)化算法,。對6個基礎(chǔ)逆運動學方程作變換,采用符號運算預處理得到14個逆運動學方程,,避免大量浮點數(shù)計算累積誤差,。利用其中6個方程與關(guān)節(jié)變量3無關(guān)的特點,,將目標矩陣從24階降低到16階,包含的關(guān)節(jié)變量從3個增加到4個,。 把一元16次方程求根問題轉(zhuǎn)換為矩陣特征分解問題,,并選取較高數(shù)量級的相關(guān)數(shù)據(jù)元素計算關(guān)節(jié)變量,進一步提高了算法精度,。以一般6R機器人為例,,求解結(jié)果表明,提出的算法能夠得到具有任意期望精度的最多16組實數(shù)逆運動學解,。

    Abstract:

    In order to improve the accuracy and efficiency of the inverse kinematics algorithm for general 6R robots, an optimized algorithm was proposed based on symbolic processing and matrix decomposition. The 6 basic inverse kinematics equations were transformed, and symbolic preprocessing was applied to gain 14 inverse kinematics equations without any accumulative errors caused by float point computations. By exploiting the characteristic that 6 equations among them were independent on joint variable 3, the order of the target matrix was reduced from 24 to 16, as while as the number of the included joint variables was increased from 3 to 4. The problem of solving a polynomial of degree 16 was optimized to decomposing a matrix of order 16, and elements with the higher magnitude were selected for calculating the joint variables, so the accuracy was enhanced in a further step. Experiments on general 6R robots show that, the proposed inverse kinematics algorithm can seek 16 real solutions at most with any required accuracy.

    參考文獻
    相似文獻
    引證文獻
引用本文

劉松國,朱世強,王宣銀,程永倫.一般6R機器人的高精度逆運動學優(yōu)化算法[J].農(nóng)業(yè)機械學報,2007,38(11):118-122.[J]. Transactions of the Chinese Society for Agricultural Machinery,2007,38(11):118-122.

復制
分享
文章指標
  • 點擊次數(shù):
  • 下載次數(shù):
  • HTML閱讀次數(shù):
  • 引用次數(shù):
歷史
  • 收稿日期:
  • 最后修改日期:
  • 錄用日期:
  • 在線發(fā)布日期:
  • 出版日期:
文章二維碼